





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "pegasus"
							lowerCamelCase_							     =       ["past_key_values"]
							lowerCamelCase_							     =       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


							def __init__(				self     :Any				,					__A     :Optional[int]=5_0265				,					__A     :int=1024				,					__A     :Union[str, Any]=12				,					__A     :Union[str, Any]=4096				,					__A     :Tuple=16				,					__A     :Union[str, Any]=12				,					__A     :Tuple=4096				,					__A     :int=16				,					__A     :List[Any]=0.0				,					__A     :Tuple=0.0				,					__A     :Dict=True				,					__A     :List[str]=True				,					__A     :Optional[int]="gelu"				,					__A     :int=1024				,					__A     :Optional[Any]=0.1				,					__A     :List[Any]=0.0				,					__A     :str=0.0				,					__A     :List[str]=0.0_2				,					__A     :str=0				,					__A     :str=False				,					__A     :List[Any]=0				,					__A     :int=1				,					__A     :str=1				,					**__A     :Tuple				,					) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   vocab_size
									SCREAMING_SNAKE_CASE__								=   max_position_embeddings
									SCREAMING_SNAKE_CASE__								=   d_model
									SCREAMING_SNAKE_CASE__								=   encoder_ffn_dim
									SCREAMING_SNAKE_CASE__								=   encoder_layers
									SCREAMING_SNAKE_CASE__								=   encoder_attention_heads
									SCREAMING_SNAKE_CASE__								=   decoder_ffn_dim
									SCREAMING_SNAKE_CASE__								=   decoder_layers
									SCREAMING_SNAKE_CASE__								=   decoder_attention_heads
									SCREAMING_SNAKE_CASE__								=   dropout
									SCREAMING_SNAKE_CASE__								=   attention_dropout
									SCREAMING_SNAKE_CASE__								=   activation_dropout
									SCREAMING_SNAKE_CASE__								=   activation_function
									SCREAMING_SNAKE_CASE__								=   init_std
									SCREAMING_SNAKE_CASE__								=   encoder_layerdrop
									SCREAMING_SNAKE_CASE__								=   decoder_layerdrop
									SCREAMING_SNAKE_CASE__								=   use_cache
									SCREAMING_SNAKE_CASE__								=   encoder_layers
									SCREAMING_SNAKE_CASE__								=   scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__A				,					eos_token_id=__A				,					is_encoder_decoder=__A				,					decoder_start_token_id=__A				,					forced_eos_token_id=__A				,					**__A				,					)


							@property
							def   _snake_case	(				self     :int		) -> int:



									"""simple docstring"""



									return self.encoder_attention_heads


							@property
							def   _snake_case	(				self     :str		) -> int:



									"""simple docstring"""



									return self.d_model






"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






